Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Generally speaking, the larger the sigma value, the more blurry the result. A larger sigma will tend to get rid of more noise in the image, @@ -807,7 +807,7 @@
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
The histogram for the data/shapes-02.jpg image can be
shown with
Here are the commands to create and view the binary mask
One approach we might take is to try to completely mask out a region from each image, particularly, the area containing the white circle and @@ -964,7 +964,7 @@
We can apply a simple binary thresholding with a threshold
t=0.95 to remove the label and circle from the image. We
@@ -1020,7 +1020,7 @@
The & operator above means that we have defined a
logical AND statement. This combines the two tests of pixel intensities
@@ -1079,7 +1079,7 @@
Here is the code to create the grayscale histogram:
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
If you are using an older version of Matplotlib you might get a warning @@ -795,7 +795,7 @@
As you might have guessed, the return value count
already contains the number of objects found in the image. So it can
@@ -906,7 +906,7 @@
The histogram can be plotted with
One way to count only objects above a certain area is to first create a list of those objects, and then take the length of that list as the @@ -1047,7 +1047,7 @@
To remove the small objects from the labeled image, we change the value of all pixels that belong to the small objects to the background @@ -1151,7 +1151,7 @@
We already know how to get the areas of the objects from the
regionprops. We just need to insert a zero area value for
@@ -1259,7 +1259,7 @@
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
First, let’s work through the process for one image:
Here is a modified function with the requested features. Note when calculating the Otsu threshold we don’t include the very bright pixels @@ -639,7 +639,7 @@
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)
Generally speaking, the larger the sigma value, the more blurry the result. A larger sigma will tend to get rid of more noise in the image, @@ -4009,7 +4009,7 @@
The histogram for the data/shapes-02.jpg image can be
shown with
Here are the commands to create and view the binary mask
One approach we might take is to try to completely mask out a region from each image, particularly, the area containing the white circle and @@ -4696,7 +4696,7 @@
We can apply a simple binary thresholding with a threshold
t=0.95 to remove the label and circle from the image. We
@@ -4752,7 +4752,7 @@
The & operator above means that we have defined a
logical AND statement. This combines the two tests of pixel intensities
@@ -4831,7 +4831,7 @@
Here is the code to create the grayscale histogram:
If you are using an older version of Matplotlib you might get a warning @@ -5356,7 +5356,7 @@
As you might have guessed, the return value count
already contains the number of objects found in the image. So it can
@@ -5471,7 +5471,7 @@
The histogram can be plotted with
One way to count only objects above a certain area is to first create a list of those objects, and then take the length of that list as the @@ -5616,7 +5616,7 @@
To remove the small objects from the labeled image, we change the value of all pixels that belong to the small objects to the background @@ -5720,7 +5720,7 @@
We already know how to get the areas of the objects from the
regionprops. We just need to insert a zero area value for
@@ -5864,7 +5864,7 @@
First, let’s work through the process for one image:
Here is a modified function with the requested features. Note when calculating the Otsu threshold we don’t include the very bright pixels @@ -6083,7 +6083,7 @@
Materials licensed under CC-BY 4.0 by the authors
Template licensed under CC-BY 4.0 by The Carpentries
-Built with sandpaper (0.16.9), pegboard (0.7.6), and varnish (1.0.4)
+Built with sandpaper (0.16.10), pegboard (0.7.7), and varnish (1.0.5)